Comparer des méthodes

	Arbre de décision explicable	Arbre de décision	Régression logistique	XGBoost
Domaine	Apprentissage automatique	Apprentissage automatique	Statistiques de recherche	Apprentissage automatique
Famille	Machine learning	Machine learning	Process / pipeline	Machine learning
Année d'origine	1984 (CART); XAI framing formalized 2010s–2020s	1984	1958	2016
Auteur d'origine	Breiman, L.; Friedman, J.; Olshen, R. A.; Stone, C. J.	Breiman, Friedman, Olshen & Stone	David Roxbee Cox	Chen, T. & Guestrin, C.
Type	Interpretable supervised learning model	Recursive partitioning (if-then rules)	Method	Ensemble (gradient-boosted decision trees)
Source fondatrice	Breiman, L., Friedman, J., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Wadsworth & Brooks/Cole. ISBN: 978-0-412-04841-8	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Alias	XDT, interpretable decision tree, rule-based decision tree, transparent decision tree	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	logit model, binomial logistic regression, LR	XGBoost, extreme gradient boosting, scalable tree boosting
Apparentées	4	5	3	5
Résumé	An Explainable Decision Tree is a classification or regression tree deliberately grown to be shallow, readable, and auditable — producing a finite set of if-then rules that a human can verify without additional tools. It sits at the intersection of predictive modelling and Explainable AI (XAI), chosen when stakeholders must understand and trust every prediction the model makes.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.